Yaowei Zheng

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 2 papers; trusts 104 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #27,693 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #17,030 of 21,575 by trust here, #20,271 by citations
• Optimization & Training — 25 citations into it
• Model Architectures — 11 citations into it
• Natural Language Processing — 11 citations into it
• Scaling & Learning Theory — 10 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Yaowei Zheng trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Ilya Sutskever
6. Yoshua Bengio
7. Christian Szegedy
8. Yann LeCun
9. Sergey Ioffe
10. Ross B. Girshick

Who trusts Yaowei Zheng overall

1. David Stutz
2. Weihao Zeng
3. Matthias Hein
4. Wenxuan Huang

Papers from Yaowei Zheng's perspective

Ordered by how strongly the researchers Yaowei Zheng trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Regularizing Neural Networks via Adversarial Model Perturbation (2020) — 116 citations
• LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024) — 1,832 citations